Technologies for software basic block similarity analysis

ABSTRACT

Technologies for analyzing software similarity include a computing device having access to a collection of sample software. The computing device identifies a number of code segments, such as basic blocks, within the software. The computing device normalizes each code segment by extracting the first data element of each computer instruction within the code segment. The first data element may be the first byte. The computing device calculates a probabilistic feature hash signature for each normalized codes segment. The computing device may filter out known-good code segments by comparing signatures with a probabilistic hash filter generated from a collection of known-good software. The computing device calculates a similarity value between each pair of unfiltered, normalized code segments. The computing device generates a graph including the normalized code segments and the similarity values. The computing device may cluster the graph using a force-based clustering algorithm. Other embodiments are described and claimed.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH

This invention was made with government support under United States AirForce Academy agreement number FA7000-11-2-0001. The government hascertain rights in the invention.

BACKGROUND

Malicious software (“malware”), such as computer viruses, worms, andtrojans, is a serious threat to current computing systems. Additionally,new malware variants are being created constantly. Typical malwaredetection systems identify malware and malware variants using filesignature scanning to identify particular binary files associated withmalware. Typically, updated malware signatures are required to detecteven minor malware variants.

Many malware researchers believe that many new malware variants may becreated by the same individuals or groups, for example through sourcecode reuse. However, typical file signature scanning may not detectsimilarities between malware caused by code reuse. Currently, theprovenance of a particular variant of malware may be determined, forexample, by manually examining textual strings or domain names includedin the malware binary. Sliding window hashes have been used as a meansto determine compiled binary code similarity. One such algorithm is CMUBitshred, described in Jiyong Jang & David Brumley, BitShred: Fast,Scalable Code Reuse Detection in Binary Code, Carnigie Mellon UniversityTechnical Report, CMU-CyLab-10-006 (2009).

BRIEF DESCRIPTION OF THE DRAWINGS

The concepts described herein are illustrated by way of example and notby way of limitation in the accompanying figures. For simplicity andclarity of illustration, elements illustrated in the figures are notnecessarily drawn to scale. Where considered appropriate, referencelabels have been repeated among the figures to indicate corresponding oranalogous elements.

FIG. 1 is a simplified block diagram of at least one embodiment of acomputing device for analyzing software basic block similarity;

FIG. 2 is a simplified block diagram of at least one embodiment of anenvironment of the computing device of FIG. 1;

FIG. 3 is a simplified flow diagram of at least one embodiment of amethod for analyzing software basic block similarity that may beexecuted by the computing device of FIGS. 1 and 2;

FIG. 4 is a schematic diagram of basic block normalization that may beperformed by the method of FIG. 3;

FIG. 5 is a schematic diagram of probabilistic hash fingerprintgeneration that may be performed by the method of FIG. 3;

FIG. 6 is a simplified flow diagram of at least one embodiment of amethod for generating a known-good filter that may be executed by thecomputing device of FIGS. 1 and 2;

FIG. 7 is a chart illustrating sample results that may be achieved bythe computing device of FIGS. 1 and 2; and

FIG. 8 is a plot illustrating a sample graph that may be generated bythe computing device of FIGS. 1 and 2.

DETAILED DESCRIPTION OF THE DRAWINGS

While the concepts of the present disclosure are susceptible to variousmodifications and alternative forms, specific embodiments thereof havebeen shown by way of example in the drawings and will be describedherein in detail. It should be understood, however, that there is nointent to limit the concepts of the present disclosure to the particularforms disclosed, but on the contrary, the intention is to cover allmodifications, equivalents, and alternatives consistent with the presentdisclosure and the appended claims.

References in the specification to “one embodiment,” “an embodiment,”“an illustrative embodiment,” etc., indicate that the embodimentdescribed may include a particular feature, structure, orcharacteristic, but every embodiment may or may not necessarily includethat particular feature, structure, or characteristic. Moreover, suchphrases are not necessarily referring to the same embodiment. Further,when a particular feature, structure, or characteristic is described inconnection with an embodiment, it is submitted that it is within theknowledge of one skilled in the art to effect such feature, structure,or characteristic in connection with other embodiments whether or notexplicitly described. Additionally, it should be appreciated that itemsincluded in a list in the form of “at least one A, B, and C” can mean(A); (B); (C); (A and B); (A and C); (B and C); or (A, B, and C).Similarly, items listed in the form of “at least one of A, B, or C” canmean (A); (B); (C); (A and B); (A and C); (B and C); or (A, B, and C).

The disclosed embodiments may be implemented, in some cases, inhardware, firmware, software, or any combination thereof. The disclosedembodiments may also be implemented as instructions carried by or storedon a transitory or non-transitory machine-readable (e.g.,computer-readable) storage medium, which may be read and executed by oneor more processors. A machine-readable storage medium may be embodied asany storage device, mechanism, or other physical structure for storingor transmitting information in a form readable by a machine (e.g., avolatile or non-volatile memory, a media disc, or other media device).

In the drawings, some structural or method features may be shown inspecific arrangements and/or orderings. However, it should beappreciated that such specific arrangements and/or orderings may not berequired. Rather, in some embodiments, such features may be arranged ina different manner and/or order than shown in the illustrative figures.Additionally, the inclusion of a structural or method feature in aparticular figure is not meant to imply that such feature is required inall embodiments and, in some embodiments, may not be included or may becombined with other features.

Referring now to FIG. 1, an illustrative computing device 100 forsoftware analysis includes a processor 120, an I/O subsystem 122, amemory 124, and a data storage device 126. In use, as described below,the computing device 100 is configured to analyze a collection ofexecutable software, such as malware or suspected malware. The computingdevice 100 determines similarity values between normalized codesegments, such as basic blocks, of the software using an n-gram analysisalgorithm. The computing device 100 may filter out known-good or benigncode segments based on similarity to known-good sample software. Thecomputing device 100 may generate a clustered graph of the basic blocksof the software. A user of the computing device 100 may visually orinteractively analyze the graph to identify similarity between analyzedsoftware. The computing device 100 may allow the user to identifyrelated software based on code reuse, algorithm similarity, and otherfeatures that may be used to identify the provenance of the software.Normalizing the code segments may improve matching accuracy by reducingwaterfall fouling caused by minor changes to the executable code.Filtering known-good code segments may improve performance, allowing forautomated or semiautomated analysis of emerging threats.

The computing device 100 may be embodied as any type of device capableof analyzing software and otherwise performing the functions describedherein. For example, the computing device 100 may be embodied as,without limitation, a laptop computer, a notebook computer, a tabletcomputer, a smartphone, a mobile computing device, a computer, a desktopcomputer, a workstation, a server computer, a distributed computingsystem, a multiprocessor system, a consumer electronic device, a smartappliance, and/or any other computing device capable of analyzingsoftware code segments. As shown in FIG. 1, the illustrative computingdevice 100 includes the processor 120, the I/O subsystem 122, the memory124, and the data storage device 126. Of course, the computing device100 may include other or additional components, such as those commonlyfound in a workstation (e.g., various input/output devices), in otherembodiments. Additionally, in some embodiments, one or more of theillustrative components may be incorporated in, or otherwise form aportion of, another component. For example, the memory 124, or portionsthereof, may be incorporated in the processor 120 in some embodiments.

The processor 120 may be embodied as any type of processor capable ofperforming the functions described herein. For example, the processormay be embodied as a single or multi-core processor(s), digital signalprocessor, microcontroller, or other processor or processing/controllingcircuit. Similarly, the memory 124 may be embodied as any type ofvolatile or non-volatile memory or data storage capable of performingthe functions described herein. In operation, the memory 124 may storevarious data and software used during operation of the computing device100 such operating systems, applications, programs, libraries, anddrivers. The memory 124 is communicatively coupled to the processor 120via the I/O subsystem 122, which may be embodied as circuitry and/orcomponents to facilitate input/output operations with the processor 120,the memory 124, and other components of the computing device 100. Forexample, the I/O subsystem 122 may be embodied as, or otherwise include,memory controller hubs, input/output control hubs, firmware devices,communication links (i.e., point-to-point links, bus links, wires,cables, light guides, printed circuit board traces, etc.) and/or othercomponents and subsystems to facilitate the input/output operations. Insome embodiments, the I/O subsystem 122 may form a portion of asystem-on-a-chip (SoC) and be incorporated, along with the processor120, the memory 124, and other components of the computing device 100,on a single integrated circuit chip.

The data storage device 126 may be embodied as any type of device ordevices configured for short-term or long-term storage of data such as,for example, memory devices and circuits, memory cards, hard diskdrives, solid-state drives, or other data storage devices. The datastorage device 126 may store software to be analyzed, includingcollections of potentially malicious software (malware) or known-goodsoftware (goodware).

The computing device 100 may also include a communication subsystem 128,which may be embodied as any communication circuit, device, orcollection thereof, capable of enabling communications between thecomputing device 100 and other remote devices over a computer network(not shown). The communication subsystem 128 may be configured to useany one or more communication technology (e.g., wired or wirelesscommunications) and associated protocols (e.g., Ethernet, Bluetooth®,Wi-Fi®, WiMAX, etc.) to effect such communication.

Additionally, the computing device 100 may include a display 130 thatmay be embodied as any type of display capable of displaying digitalinformation such as a liquid crystal display (LCD), a light emittingdiode (LED), a plasma display, a cathode ray tube (CRT), or other typeof display device. In some embodiments, the computing device 100 mayalso include one or more peripheral devices 132. The peripheral devices132 may include any number of additional input/output devices, interfacedevices, and/or other peripheral devices.

Referring now to FIG. 2, in the illustrative embodiment, the computingdevice 100 establishes an environment 200 during operation. Theillustrative embodiment 200 includes a static analysis module 204, anormalization module 208, a signature module 212, a filter module 216, asimilarity module 222, and a visualization module 226. The variousmodules of the environment 200 may be embodied as hardware, firmware,software, or a combination thereof. For example, each of the modules,logic, and other components of the environment 200 may form a portionof, or otherwise be established by, the processor 120 or other hardwarecomponents of the computing device 100.

The static analysis module 204 is configured to identify a plurality ofcode segments 206 within malware samples data 202. The malware samplesdata 202 may be embodied as any collection of executable code, objectcode, bytecode, executable files, library files, or other binary codethat may potentially contain malware. The malware samples data 202 maybe supplied by a user of the computing device 100, or may be identifiedautomatically. Malware is often obfuscated or “packed,” for example bycompressing or encrypting executable code within an executable file. Insome embodiments, the static analysis module 204 may be configured tounpack or otherwise deobfuscate the malware samples data 202. Each codesegment 206 may be embodied as a basic block, an executable segment, afunction, an algorithm, or any other subdivision of the malware samplesdata 202. As described below, the computing device 100 analyzes the codesegments 206 for similarity.

The normalization module 208 is configured to extract the first bytefrom each instruction of a code segment 206 to generate a normalizedcode segment 210. In some embodiments, the normalization module 208 maybe configured to extract a different data element from each of the codesegments 206. Thus, each normalized code segment 210 includes a singledata element, for example a single byte, representing each instructionof a corresponding code segment 206.

The signature module 212 is configured to determine a probabilistic hashsignature 214 for each normalized code segment 210 using an n-gramanalysis algorithm. An n-gram may be embodied as a group of nconsecutive bytes selected from the normalized code segment 210. Asdescribed below, the signature module 212 may apply a sliding window ofwidth n to each normalized code segment 210, determining a hash valuefor each window. Each signature 214 may be embodied as a bit vector,with each set bit corresponding to a hash value for an n-gram foundwithin the associated normalized code segment 210.

The filter module 216 is configured to filter out normalized codesegments 210 as a function of known-good software (“goodware”) samplesdata 218. The goodware samples data 218 may be embodied as anycollection of executable code, object code, bytecode, executable files,library files, or other binary code that is trusted or otherwise knownnot to contain malware. The filter module 216 is configured to generatefilter data 220 as a function of the goodware samples data 218. Thefilter data 220 may be embodied as any database, bit field, or otherdata structure capable of storing a collection of probabilistic hashsignatures, similar to the signatures 214. The filter module 216 (or, insome embodiments the static analysis module 204, the normalizationmodule 208, and/or the signature module 212) is configured to generatethe filter data 220 using the same n-gram analysis algorithm used togenerate the signatures 214. The filter module 216 is configured tofilter the normalized code segments 210 by determining whether theassociated signatures 214 are included in the filter data 220.

The similarity module 222 is configured to determine a similarity value224 between each normalized code segment 210 and the other normalizedcode segments 210. Each similarity value 224 may be determined as afunction of the signatures 214 of each normalized code segment 210. Forexample, the similarity value 224 may be embodied as an approximateJaccard index, as described below. In some embodiments, the similaritymodule 222 may calculate similarity values 224 only for unfilterednormalized code segments 210; that is, only for normalized code segments210 that have not been filtered out by the filter module 216.

The visualization module 226 is configured to generate a graph includingthe normalized code segments 210 and the similarity values 224. Thevisualization module 226 may cluster the graph using a force-basedlayout algorithm. In some embodiments, a user may interactively view,manipulate, or otherwise analyze the graph, for example using thedisplay 130 of the computing device 100.

Although illustrated as including both the normalization module 208 andthe filter module 216, it should be understood that in some embodimentsthe environment 200 may include the normalization module 208 but not thefilter module 216. In those embodiments, the similarity module 222 maydetermine similarity values 224 for all normalized code segments 210,without filtering. Similarly, in some embodiments the environment 200may include the filter module 216 but not the normalization module 208.In those embodiments, the signature module 212 may generate signatures214 for the code segments 206 rather than the normalized code segments210.

Referring now to FIG. 3, in use, the computing device 100 may execute amethod 300 for analyzing similarity of software components. The method300 begins in block 302, in which the computing device 100 selectspotential malicious software (malware) samples data 202 for analysis.The computing device 100 may use any technique for selecting malwaresamples data 202 for analysis. For example, in some embodiments, a userof the computing device 100 may provide malware samples, such assuspected malware or a newly discovered malware variant. Additionally oralternatively, in some embodiments the computing device 100 may selectthe malware samples data 202 in an automated fashion, for example byanalyzing all malware samples included in the malware samples data 202.Of course, although the method 300 illustrates analysis of potentialmalware, it should be understood that in other embodiments any softwaremay be analyzed. For example, in some embodiments untrusted orunverified code may be analyzed for similarity with known malware, orknown-good code may be analyzed for similarity (e.g., to detectunauthorized copying). In some embodiments, in block 304, the computingdevice 100 may deobfuscate, or “unpack,” the malware samples data 202,if necessary. The computing device 100 may use a modular plugin or otherunpacker to deobfuscate the malware samples data 202, allowing thecomputing device 100 to adapt to varying forms of code obfuscation.

In block 306, the computing device 100 statically analyzes the malwaresamples data 202 to identify code segments 206 to be analyzed. That is,the computing device 100 analyzes the malware samples data 202 withoutexecuting the malware samples. The computing device 100 may analyze anytype of code segment 206 of the malware samples. For example, thecomputing device 100 may analyze entire text segments of the malwaresamples, such as .text segments of Windows executables or dynamic linklibraries (DLLs), text segments of ELF-format executables, text segmentsof Mach-O executables, or any other executable segment of a malwaresample. In block 308, in some embodiments, the computing device 100 mayidentify basic blocks 206 included in the malware samples. The computingdevice 100 may use any suitable technique to identify the basic blocks206. For example, the computing device 100 may use a static analysistool such as IDA Professional to identify the basic blocks 206. In block310, in some embodiments, the computing device 100 may identify entirealgorithms 206 within the malware samples. Algorithms 206 may includeloops, branches, and other control flow structures, and thus may spanmore than one basic block. Again, the computing device 100 may use anysuitable technique to identify the algorithms 206 as discussed above.

In block 312, the computing device 100 normalizes the code segments 206.In the illustrative embodiment, the computing device 100 normalizes thecode by extracting the first byte of every computer instruction includedin the code segment 206. Thus, for each code segment 206 included in themalware samples, the computing device 100 generates a correspondingnormalized code segment 210. For example, the computing device 100 mayextract the first byte of every computer instruction included in a basicblock 206 or in an algorithm 206. Extracting the first byte of eachcomputer instruction may allow each instruction to be represented by aconstant amount of data. For example, many computer architectures,including Intel® IA-32 Intel® 64, use variable-length instructions, andextracting the first byte of each computer instruction represents eachinstruction using a single byte. Additionally, extracting the first byteof each instruction may disregard the operands used by instructions,such as registers or memory addresses, while still retaining the overallstructure of a code segment 210. Thus, by disregarding operands that maychange due to small changes in the code (e.g., memory address offsetsthat change due the addition of a function argument), waterfall foulingmay be avoided and match accuracy may be improved. Additionally,although in the illustrative embodiment the computing device 100extracts the first byte of each instruction, it should be understoodthat in other embodiments the computing device 100 may extract anyconstant-sized data element of each instruction. For example, in someembodiments the computing device 100 may extract opcodes, words,nibbles, or any other segment from each instruction.

Referring now to FIG. 4, a schematic diagram 400 illustrates anembodiment of normalization of a code segment 206 to generate anormalized code segment 210. As shown, the illustrative code segment 206includes seven processor instructions. Each processor instruction may berepresented by a variable number of bytes; in the illustrative example,the illustrative code segment 206 occupies 25 bytes. As shown, the firstbyte from each instruction is extracted, in order, to generate thenormalized code segment 210. The normalized code segment 210 occupiesseven bytes.

Referring back to FIG. 3, in block 314, the computing device 100calculates a signature 214 for each normalized code segment 210. Inparticular, the computing device 100 may calculate a probabilistic hashsignature 214 for each normalized code segment 210 using an n-gramanalysis algorithm as described in blocks 316 through 322. In block 316,for each normalized code segment 210 the computing device 100initializes a bit vector to all zeroes. The bit vector is used to storethe probabilistic hash signature 214 corresponding to that normalizedcode segment 210.

In block 318, the computing device 100 generates all n-grams for thenormalized code segment 210. Each n-gram may be embodied as a group of nconsecutive bytes selected from the normalized code segment 210. Thecomputing device 100 may generate the n-grams, for example, by applyinga sliding window of size n to the normalized code segment 210. Thecomputing device 100 may generate the n-grams in space or in time; forexample the computing device 100 may generate an array of all n-grams ormay iterate through all n-grams.

In block 320, the computing device 100 calculates a hash value for eachof the n-grams. The computing device 100 may use any appropriate hashfunction to calculate the n-grams. For example, the computing device 100may apply an MD5 hash function to generate the hash value for eachn-gram.

In block 322, for each n-gram, the computing device 100 indexes the bitvector using the corresponding hash value and sets the indexed bit ofthe bit vector. For example, the computing device 100 may determine theindex by dividing the hash value modulo the width of the bit vector. Inother words, if the bit vector has width x, the index may be determinedas the x least-significant bits of the hash value. The resulting bitvector represents the probabilistic hash signature 214 of all of then-grams included in the normalized code segment 210. Ideally, each setbit in the bit vector would indicate that the n-gram of thecorresponding hash value was found in the normalized code segment 210.However, because some hash values may collide, that is, index to thesame bit of the bit vector, the signature 214 is a probabilisticindication of whether the n-gram of the corresponding hash value wasfound in the normalized code segment 210.

Referring now to FIG. 5, the schematic diagram 500 illustrates anembodiment of the calculation of the signature 214 for a normalized codesegment 210. As shown, an n-gram 502 includes the first five bytes ofthe normalized code segment 210. Thus, in the illustrative embodiment, nis equal to five; of course, in other embodiments other values of n maybe used. Therefore, as described above, the n-gram 502 also correspondsto the first five instructions of the corresponding code segment 206.The n-gram 502 is fed as input to a hash function 504. The output of thehash function 504 is used to index into a bit vector of the signature214. The bit at that position—in the illustrative example, the fourthbit—is set to “1.” The rest of the signature 214 may be calculated bysliding a window of width n through the rest of the normalized codesegment 210. As shown, the next n-gram 506 includes five bytes selectedfrom positions two through six of the normalized code segment 210. Then-gram 506 is fed to the hash function 504, and the resulting hash valueis used to index the signature 214 and set the appropriate bit.Calculation of the signature 214 may continue to the n-gram 508, whichincludes the last five bytes of the normalized code segment 210. Then-gram 508 is similarly fed to the hash function 504, and the resultinghash value is used to index the signatures 214 and set the appropriatebit.

Referring back to FIG. 3, after calculating the signature 214 for eachnormalized code segment 210, in block 324 the computing device 100 mayfilter out known-good code segments 210 using the filter data 220. Asdescribed above, the filter data 220 may include a set of probabilistichash values corresponding to a collection of normalized code segments ofthe goodware samples data 218. The goodware samples data 218 mayinclude, for example, operating system code, library code, commercialapplications, or other software that is known to be safe. Calculation ofthe filter data 220 is further described below in connection with FIG.6. The computing device 100 may perform a set-inclusion operation todetermine whether each signature 214 is included in the filter data 220.If the signature 214 matches the filter data 220, the correspondingnormalized code segment 210 may be marked as filtered or otherwisedistinguished from unfiltered normalized coded segments 210 that do notmatch the filter data 220. In some embodiments, the computing device 100may not apply the filter data 220 or may apply empty filter data 220. Inthose embodiments, all normalized code segments 210 may be designatedunfiltered.

In block 326, for each unfiltered normalized code segment 210, thecomputing device 100 determines a similarity value 224 in relation toall of the other unfiltered normalized code segments 210. Thus,calculation of the similarity values 224 is of O(n²) complexity.Filtering out known-good normalized code segments 210, as describedabove in block 324, is a linear operation, of O(n) complexity. Thus,filtering may reduce total calculation time by reducing the problemspace for the calculation of similarity values 224. The computing device100 may calculate the similarity value 224 as an approximate Jaccardindex of the features of the normalized code segments 210. All of then-grams of a normalized code segment 210 may be considered to be featureset F₁ of that normalized code segment 210. Thus, each bit of thecorresponding signature 214 is a probabilistic indication that aparticular feature, or n-gram, was present in the normalized codesegment 210. Accordingly, the computing device 100 may calculate anapproximate Jaccard index similarity value as the ratio of the number ofset bits of the intersection of the signatures 214 of each normalizedcode segment 210 to the number of set bits of the union of thesignatures 214 of each normalized code segment 210. That similarityvalue 224 represents the ratio of matching n-grams to possible matchesfor two signatures 214. One potential embodiment of a calculation of theapproximate Jaccard index is illustrated below in Equation 1. B_(a) andB_(b) represent bit vectors of the signatures 214. S( ) represents afunction to count the number of set bits. J(F_(a), F_(b)) represents theJaccard index of two feature sets, and may have values from zero to one.

$\begin{matrix}{{J\left( {F_{a},F_{b}} \right)} \approx \frac{S\left( {B_{a}\bigwedge B_{b}} \right)}{S\left( {B_{a}\bigvee B_{b}} \right)}} & (1)\end{matrix}$

In block 328, the computing device 100 plots the similarity betweennormalized code segments 210 as a graph. The computing device 100 maygenerate a node in the graph for each normalized code segment 210. Thegraph may include parent nodes corresponding to each malware samplewithin the malware samples data 202, and each parent node may beassociated with child nodes for the appropriate normalized code segments210. The computing device 100 may generate a node for both filtered andunfiltered normalized code segments 210. For each unfiltered normalizedcode segment 210, the computing device 100 may generate an edgeconnecting the normalized code segment 210 to each other normalized codesegment 210 having a similarity value 224 above a threshold similarityvalue. Accordingly, because no similarity values 224 are calculated forfiltered normalized code segments 210, no edges connect to the filterednormalized code segments 210. The computing device 100 may present thegraph to a user, for example by displaying the graph interactively usingthe display 130.

In some embodiments, in block 330, the computing device 100 may clusterthe normalized code segments 210 in the graph using an unsupervisedforce-based algorithm, also known as a push-pull algorithm. The clustersidentified in the graph may identify similar groups of normalized codesegments 210 and thus may identify similarities between malware samples.The computing device 100 may perform the clustering analysis in anautomated or offline manner, and/or may perform the analysisinteractively using the display 130. After analyzing the normalized codesegments 210, the method 300 loops back to block 302 select additionalmalware samples for analysis.

Referring now to FIG. 6, in use, the computing device 100 may execute amethod 600 for generating a known-good filter. The method 600 begins inblock 602, in which the computing device 100 selects goodware samplesdata 218. The goodware samples data 218 may include, for example,executable files for a known-good operating system of the computingdevice 100. The goodware samples data 218 may include all of the binaryfiles included in a system directory (e.g., the SYSTEM32 directory onWindows or the “/bin” directory on Unix variants). In some embodiments,the goodware samples data 218 may include a known-good applicationbinary, such as a web browser.

In block 604, similar to as described above in connection with block 306of FIG. 3, the computing device 100 statically analyzes the goodwaresamples data 218 to identify code segments 206 to be analyzed. Thecomputing device 100 may analyze any type of code segment 206 of thegoodware samples. For example, the computing device 100 may analyzeentire text segments of the malware samples, such as .text segments ofWindows executables or dynamic link libraries (DLLs), text segments ofELF-format executables, text segments of Mach-O executables, or anyother executable segment of a goodware sample. In block 606, in someembodiments, the computing device 100 may identify basic blocks 206included in the goodware samples. The computing device 100 may use astatic analysis tool such as IDA Professional to identify the basicblocks 206. In block 608, in some embodiments, the computing device 100may identify entire algorithms 206 within the goodware samples.Algorithms 206 may include loops, branches, and other control flowstructures, and thus may span more than one basic block.

In block 610, similar to as described above in connection with block 312of FIG. 3, the computing device 100 normalizes the code segments 206 byextracting the first byte of every computer instruction included in thecode segment. Thus, for each code segment 206 included in the goodwaresamples, the computing device 100 generates a corresponding normalizedcode segment 210. For example, the computing device 100 may extract thefirst byte of every computer instruction included in a basic block 206or in an algorithm 206.

In block 612, the computing device 100 initializes the filter data 220to the empty set. For example, in some embodiments, the computing device100 may initialize a large bit field of the filter data 220 to allzeroes. In block 614, the computing device 100 calculates aprobabilistic hash signature of each normalized code segment 210 of thegoodware sample data 218. The computing device 100 applies the samen-gram analysis used to generate the signatures 214, as described abovein connection with blocks 314 through 322 of FIG. 3. In block 616, thecomputing device 100 stores each signature associated with thenormalized code segments 210 of the goodware sample data 218 into thefilter data 220. The computing device 100 may store the signatures inany appropriate format. For example, the computing device 100 may storea bit vector corresponding to the signature in a large bit fieldcorresponding to the filter data 220. After being stored in the filterdata 220, the computing device 100 may test the filter data 220 for setinclusion, as described above in connection with block 324 of FIG. 3.After generating the filter data 220, the method 600 loops back to block602, in which additional goodware samples data 218 may be selected.

Referring now to FIG. 7, the chart 700 illustrates sample results thatmay be achieved using technologies described in this disclosure. Inparticular, the chart 700 illustrates illustrative results that may beachieved for a sample set of malware samples data 202 including 10,000basic blocks. The chart 700 illustrates the number of matching basicblocks on the y-axis and the percentage similarity threshold used todetermine a match on the x-axis. The curve 702 illustrates matches foundconsidering all bytes of the basic blocks. That is, the curve 702illustrates results for calculating signatures 214 based on full codesegments 206 rather than normalized code segments 210. The curve 704illustrates matches found considering the first byte of each basicblock. That is, the curve 704 illustrates results for calculatingsignatures 214 based on normalized code segments 210, as described abovein connection with FIG. 3. As shown, calculating hash signatures 214based on normalized code segments 210 produces many more matches at thesame matching threshold compared to calculating hash signatures 214based on code segments 206.

Referring now to FIG. 8, the diagram 800 illustrates a sample graphplotting the similarities between normalized code segments 210. In theillustrative diagram 800, each node represents a basic block, and eachedge represents a similarity value between basic blocks. Filtered basicblocks appear in the diagram 800, but are not connected to any edges.The diagram 800 has been laid out using a force-based clusteringalgorithm to help identify similarity relationships. A human operatormay analyze the diagram 800 visually and/or interactively to identifyrelationships between the normalized code segments 210 and thus betweenthe malware samples data 202.

EXAMPLES

Illustrative examples of the technologies disclosed herein are providedbelow. An embodiment of the technologies may include any one or more,and any combination of, the examples described below.

Example 1 includes a computing device for software analysis, thecomputing device comprising a static analysis module to identify aplurality of code segments within a collection of software; anormalization module to, for each code segment of the plurality of codesegments, extract a first data element from each computer instruction ofthe corresponding code segment to generate a normalized code segment; asignature module to determine a probabilistic hash signature for eachnormalized code segment using an n-gram analysis algorithm; and asimilarity module to determine a similarity value for each pair ofnormalized code segments as a function of the probabilistic hashsignatures of the corresponding pair of normalized code segments,wherein each similarity value is indicative of code similarity betweenthe corresponding pair of normalized code segments.

Example 2 includes the subject matter of Example 1, and wherein thecollection of software comprises a collection of known malware.

Example 3 includes the subject matter of any of Examples 1 and 2, andwherein the first data element of each computer instruction comprises afirst byte of each computer instruction.

Example 4 includes the subject matter of any of Examples 1-3, andwherein each code segment of the plurality of code segments comprises anexecutable section of an application binary.

Example 5 includes the subject matter of any of Examples 1-4, andwherein each code segment of the plurality of code segments comprises abasic block.

Example 6 includes the subject matter of any of Examples 1-5, andwherein to identify the plurality of code segments comprises tostatically analyze the collection of software to identify the basicblocks.

Example 7 includes the subject matter of any of Examples 1-6, andwherein each code segment of the plurality of code segments comprises analgorithm.

Example 8 includes the subject matter of any of Examples 1-7, andwherein the static analysis module is further to deobfuscate thecollection of software prior to identification of the plurality of codesegments within the collection of software.

Example 9 includes the subject matter of any of Examples 1-8, andwherein to determine the probabilistic hash signature for eachnormalized code segment using the n-gram analysis algorithm comprisesto, for each normalized code segment clear all bits of a signature bitvector associated with the corresponding normalized code segment;generate a plurality of n-grams as a function of the correspondingnormalized code segment, using a sliding window of width n; and for eachn-gram of the plurality of n-grams: (i) apply a hash function to thecorresponding n-gram to generate a hash value, (ii) determine a bitindex as a function of the hash value, and (iii) set a bit within thesignature bit vector at the bit index.

Example 10 includes the subject matter of any of Examples 1-9, andwherein the hash function comprises an MD5 hash function.

Example 11 includes the subject matter of any of Examples 1-10, andwherein to determine the bit index comprises to determine a first numberof least-significant bits of the hash value.

Example 12 includes the subject matter of any of Examples 1-11, andfurther including a filter module to filter the plurality of normalizedcode segments as a function of a plurality of known-good normalized codesegments to identify unfiltered normalized code segments of thenormalized code segments; wherein to determine the similarity value foreach pair of normalized code segments comprises to determine thesimilarity value for each pair of unfiltered normalized code segments.

Example 13 includes the subject matter of any of Examples 1-12, andwherein to filter the plurality of normalized code segments comprises todetermine whether the probabilistic hash signature of the normalizedcode segment is included in a filter bit vector generated as a functionof the plurality of known-good normalized code segments.

Example 14 includes the subject matter of any of Examples 1-13, andwherein the static analysis module is further to identify a secondplurality of code segments within a collection of known-good software;the normalization module is further to, for each code segment of thesecond plurality of code segments, extract a first data element fromeach computer instruction of the code segment to generate a known-goodnormalized code segment within the plurality of known-good normalizedcode segments; and the filter module is further to determine the filterbit vector as a function of the plurality of known-good normalized codesegments.

Example 15 includes the subject matter of any of Examples 1-14, andwherein to determine the filter bit vector comprises to determine thefilter bit vector as a function of the plurality of known-goodnormalized code segments using the n-gram analysis algorithm.

Example 16 includes the subject matter of any of Examples 1-15, andfurther including a visualization module to generate a graph includingthe similarity values.

Example 17 includes the subject matter of any of Examples 1-16, andwherein to generate the graph including the similarity values comprisesto create a node for each normalized code segment; and for each pair ofunfiltered normalized code segments, create an edge between the nodesfor the corresponding pair of unfiltered normalized code segments if thesimilarity value of the corresponding pair of unfiltered normalized codesegments has a predefined relationship with a threshold similarityvalue.

Example 18 includes the subject matter of any of Examples 1-17, andwherein the visualization module is further to cluster the graph using aforce-based clustering layout algorithm.

Example 19 includes the subject matter of any of Examples 1-18, andwherein to cluster the graph comprises to interactively display thegraph using a display of the computing device.

Example 20 includes the subject matter of any of Examples 1-19, andwherein to determine the similarity value comprises to determine anapproximate Jaccard index of the probabilistic hash signatures of eachpair of normalized code segments.

Example 21 includes a computing device for software analysis, thecomputing device comprising a static analysis module to identify aplurality of code segments within a collection of software; a signaturemodule to determine a probabilistic hash signature for each code segmentusing an n-gram analysis algorithm; a filter module to filter theplurality of code segments as a function of a plurality of known-goodcode segments to identify unfiltered code segments of the code segments;and a similarity module to determine a similarity value for each pair ofunfiltered code segments as a function of the probabilistic hashsignatures of the corresponding pair of unfiltered code segments,wherein each similarity value is indicative of code similarity betweenthe corresponding pair of unfiltered code segments.

Example 22 includes the subject matter of Example 21, and wherein tofilter the plurality of code segments comprises to determine whether theprobabilistic hash signature of the code segment is included in a filterbit vector generated as a function of the plurality of known-good codesegments.

Example 23 includes the subject matter of any of Examples 21 and 22, andwherein the filter module is further to determine the filter bit vectoras a function of the known-good code segments using the n-gram analysisalgorithm.

Example 24 includes a method for software analysis, the methodcomprising identifying, by a computing device, a plurality of codesegments within a collection of software; extracting, by the computingdevice and for each code segment of the plurality of code segments, afirst data element from each computer instruction of the correspondingcode segment to generate a normalized code segment; determining, by thecomputing device, a probabilistic hash signature for each normalizedcode segment using an n-gram analysis algorithm; and determining, by thecomputing device, a similarity value for each pair of normalized codesegments as a function of the probabilistic hash signatures of thecorresponding pair of normalized code segments, wherein each similarityvalue is indicative of code similarity between the corresponding pair ofnormalized code segments.

Example 25 includes the subject matter of Example 24, and wherein thecollection of software comprises a collection of known malware.

Example 26 includes the subject matter of any of Examples 24 and 25, andwherein extracting the first data element of each computer instructioncomprises extracting a first byte of each computer instruction.

Example 27 includes the subject matter of any of Examples 24-26, andwherein each code segment of the plurality of code segments comprises anexecutable section of an application binary.

Example 28 includes the subject matter of any of Examples 24-27, andwherein each code segment of the plurality of code segments comprises abasic block.

Example 29 includes the subject matter of any of Examples 24-28, andwherein identifying the plurality of code segments comprises staticallyanalyzing the collection of software to identify the basic blocks.

Example 30 includes the subject matter of any of Examples 24-29, andwherein each code segment of the plurality of code segments comprises analgorithm.

Example 31 includes the subject matter of any of Examples 24-30, andfurther including deobfuscating, by the computing device, the collectionof software prior to identifying the plurality of code segments withinthe collection of software.

Example 32 includes the subject matter of any of Examples 24-31, andwherein determining the probabilistic hash signature for each normalizedcode segment using the n-gram analysis algorithm comprises, for eachnormalized code segment clearing all bits of a signature bit vectorassociated with the corresponding normalized code segment; generating aplurality of n-grams as a function of the corresponding normalized codesegment, using a sliding window of width n; and for each n-gram of theplurality of n-grams: (i) applying a hash function to the correspondingn-gram to generate a hash value, (ii) determining a bit index as afunction of the hash value, and (iii) setting a bit within the signaturebit vector at the bit index.

Example 33 includes the subject matter of any of Examples 24-32, andwherein applying the hash function comprises applying an MD5 hashfunction.

Example 34 includes the subject matter of any of Examples 24-33, andwherein determining the bit index comprises determining a first numberof least-significant bits of the hash value.

Example 35 includes the subject matter of any of Examples 24-34, andfurther including filtering, by the computing device, the plurality ofnormalized code segments as a function of a plurality of known-goodnormalized code segments to identify unfiltered normalized code segmentsof the normalized code segments; wherein determining the similarityvalue for each pair of normalized code segments comprises determiningthe similarity value for each pair of unfiltered normalized codesegments.

Example 36 includes the subject matter of any of Examples 24-35, andwherein filtering the plurality of normalized code segments comprisesdetermining whether the probabilistic hash signature of the normalizedcode segment is included in a filter bit vector generated as a functionof the plurality of known-good normalized code segments.

Example 37 includes the subject matter of any of Examples 24-36, andfurther including identifying, by the computing device, a secondplurality of code segments within a collection of known-good software;for each code segment of the second plurality of code segments,extracting, by the computing device, a first data element from eachcomputer instruction of the code segment to generate a known-goodnormalized code segment within the plurality of known-good normalizedcode segments; and determining, by the computing device, the filter bitvector as a function of the plurality of known-good normalized codesegments.

Example 38 includes the subject matter of any of Examples 24-37, andwherein determining the filter bit vector comprises determining thefilter bit vector as a function of the plurality of known-goodnormalized code segments using the n-gram analysis algorithm.

Example 39 includes the subject matter of any of Examples 24-38, andfurther including generating, by the computing device, a graph includingthe similarity values.

Example 40 includes the subject matter of any of Examples 24-39, andwherein generating the graph including the similarity values comprisescreating a node for each normalized code segment; and for each pair ofunfiltered normalized code segments, creating an edge between the nodesfor the corresponding pair of unfiltered normalized code segments if thesimilarity value of the corresponding pair of unfiltered normalized codesegments has a predefined relationship with a threshold similarityvalue.

Example 41 includes the subject matter of any of Examples 24-40, andfurther including clustering, by the computing device, the graph using aforce-based clustering layout algorithm.

Example 42 includes the subject matter of any of Examples 24-41, andwherein clustering the graph comprises interactively displaying thegraph using a display of the computing device.

Example 43 includes the subject matter of any of Examples 24-42, andwherein determining the similarity value comprises determining anapproximate Jaccard index of the probabilistic hash signatures of eachpair of normalized code segments.

Example 44 includes a method for software analysis, the methodcomprising identifying, by a computing device, a plurality of codesegments within a collection of software; determining, by the computingdevice, a probabilistic hash signature for each code segment using ann-gram analysis algorithm; filtering, by the computing device, theplurality of code segments as a function of a plurality of known-goodcode segments to identify unfiltered code segments of the code segments;and determining, by the computing device, a similarity value for eachpair of unfiltered code segments as a function of the probabilistic hashsignatures of the corresponding pair of unfiltered code segments,wherein each similarity value is indicative of code similarity betweenthe corresponding pair of unfiltered code segments.

Example 45 includes the subject matter of Example 44, and filtering theplurality of code segments comprises determining whether theprobabilistic hash signature of the code segment is included in a filterbit vector generated as a function of the plurality of known-good codesegments.

Example 46 includes the subject matter of any of Examples 44 and 45, andfurther including determining, by the computing device, the filter bitvector as a function of the known-good code segments using the n-gramanalysis algorithm.

Example 47 includes a computing device comprising a processor; and amemory having stored therein a plurality of instructions that whenexecuted by the processor cause the computing device to perform themethod of any of Examples 24-46.

Example 48 includes one or more machine readable storage mediacomprising a plurality of instructions stored thereon that in responseto being executed result in a computing device performing the method ofany of Examples 24-46.

Example 49 includes a computing device comprising means for performingthe method of any of Examples 24-46.

Example 50 includes a computing device for software analysis, thecomputing device comprising means for identifying a plurality of codesegments within a collection of software; means for extracting, for eachcode segment of the plurality of code segments, a first data elementfrom each computer instruction of the corresponding code segment togenerate a normalized code segment; means for determining aprobabilistic hash signature for each normalized code segment using ann-gram analysis algorithm; and means for determining a similarity valuefor each pair of normalized code segments as a function of theprobabilistic hash signatures of the corresponding pair of normalizedcode segments, wherein each similarity value is indicative of codesimilarity between the corresponding pair of normalized code segments.

Example 51 includes the subject matter of Example 50, and wherein thecollection of software comprises a collection of known malware.

Example 52 includes the subject matter of any of Examples 50 and 51, andwherein the means for extracting the first data element of each computerinstruction comprises means for extracting a first byte of each computerinstruction.

Example 53 includes the subject matter of any of Examples 50-52, andwherein each code segment of the plurality of code segments comprises anexecutable section of an application binary.

Example 54 includes the subject matter of any of Examples 50-53, andwherein each code segment of the plurality of code segments comprises abasic block.

Example 55 includes the subject matter of any of Examples 50-54, andwherein the means for identifying the plurality of code segmentscomprises means for statically analyzing the collection of software toidentify the basic blocks.

Example 56 includes the subject matter of any of Examples 50-55, andwherein each code segment of the plurality of code segments comprises analgorithm.

Example 57 includes the subject matter of any of Examples 50-56, andfurther including means for deobfuscating the collection of softwareprior to identifying the plurality of code segments within thecollection of software.

Example 58 includes the subject matter of any of Examples 50-57, andwherein the means for determining the probabilistic hash signature foreach normalized code segment using the n-gram analysis algorithmcomprises, for each normalized code segment means for clearing all bitsof a signature bit vector associated with the corresponding normalizedcode segment; means for generating a plurality of n-grams as a functionof the corresponding normalized code segment, using a sliding window ofwidth n; and for each n-gram of the plurality of n-grams: (i) means forapplying a hash function to the corresponding n-gram to generate a hashvalue, (ii) means for determining a bit index as a function of the hashvalue, and (iii) means for setting a bit within the signature bit vectorat the bit index.

Example 59 includes the subject matter of any of Examples 50-58, andwherein the means for applying the hash function comprises means forapplying an MD5 hash function.

Example 60 includes the subject matter of any of Examples 50-59, andwherein the means for determining the bit index comprises means fordetermining a first number of least-significant bits of the hash value.

Example 61 includes the subject matter of any of Examples 50-60, andfurther including means for filtering the plurality of normalized codesegments as a function of a plurality of known-good normalized codesegments to identify unfiltered normalized code segments of thenormalized code segments; wherein the means for determining thesimilarity value for each pair of normalized code segments comprisesmeans for determining the similarity value for each pair of unfilterednormalized code segments.

Example 62 includes the subject matter of any of Examples 50-61, andwherein the means for filtering the plurality of normalized codesegments comprises means for determining whether the probabilistic hashsignature of the normalized code segment is included in a filter bitvector generated as a function of the plurality of known-good normalizedcode segments.

Example 63 includes the subject matter of any of Examples 50-62, andfurther including means for identifying a second plurality of codesegments within a collection of known-good software; for each codesegment of the second plurality of code segments, means for extracting afirst data element from each computer instruction of the code segment togenerate a known-good normalized code segment within the plurality ofknown-good normalized code segments; and means for determining thefilter bit vector as a function of the plurality of known-goodnormalized code segments.

Example 64 includes the subject matter of any of Examples 50-63, andwherein the means for determining the filter bit vector comprises meansfor determining the filter bit vector as a function of the plurality ofknown-good normalized code segments using the n-gram analysis algorithm.

Example 65 includes the subject matter of any of Examples 50-64, andfurther including means for generating a graph including the similarityvalues.

Example 66 includes the subject matter of any of Examples 50-65, andwherein the means for generating the graph including the similarityvalues comprises means for creating a node for each normalized codesegment; and for each pair of unfiltered normalized code segments, meansfor creating an edge between the nodes for the corresponding pair ofunfiltered normalized code segments if the similarity value of thecorresponding pair of unfiltered normalized code segments has apredefined relationship with a threshold similarity value.

Example 67 includes the subject matter of any of Examples 50-66, andfurther including means for clustering the graph using a force-basedclustering layout algorithm.

Example 68 includes the subject matter of any of Examples 50-67, andwherein the means for clustering the graph comprises means forinteractively displaying the graph using a display of the computingdevice.

Example 69 includes the subject matter of any of Examples 50-68, andwherein the means for determining the similarity value comprises meansfor determining an approximate Jaccard index of the probabilistic hashsignatures of each pair of normalized code segments.

Example 70 includes a computing device for software analysis, thecomputing device comprising means for identifying a plurality of codesegments within a collection of software; means for determining aprobabilistic hash signature for each code segment using an n-gramanalysis algorithm; means for filtering the plurality of code segmentsas a function of a plurality of known-good code segments to identifyunfiltered code segments of the code segments; and means for determininga similarity value for each pair of unfiltered code segments as afunction of the probabilistic hash signatures of the corresponding pairof unfiltered code segments, wherein each similarity value is indicativeof code similarity between the corresponding pair of unfiltered codesegments.

Example 71 includes the subject matter of Example 70, and wherein themeans for filtering the plurality of code segments comprises means fordetermining whether the probabilistic hash signature of the code segmentis included in a filter bit vector generated as a function of theplurality of known-good code segments.

Example 72 includes the subject matter of any of Examples 70 and 71, andfurther including means for determining the filter bit vector as afunction of the known-good code segments using the n-gram analysisalgorithm.

1. A computing device for software analysis, the computing devicecomprising: a static analysis hardware module to identify a plurality ofcode segments within a collection of software; a normalization hardwaremodule to, for each code segment of the plurality of code segments,extract a first data element from each computer instruction of acorresponding code segment to generate a normalized code segment; asignature hardware module to determine a probabilistic hash signaturefor each normalized code segment using an n-gram analysis algorithm; anda similarity hardware module to determine a similarity value for eachpair of normalized code segments as a function of the probabilistic hashsignatures of a corresponding pair of normalized code segments, whereineach similarity value is indicative of code similarity between thecorresponding pair of normalized code segments.
 2. The computing deviceof claim 1, wherein the first data element of each computer instructioncomprises a first byte of each computer instruction.
 3. The computingdevice of claim 1, wherein each code segment of the plurality of codesegments comprises a basic block.
 4. The computing device of claim 3,wherein to identify the plurality of code segments comprises tostatically analyze the collection of software to identify the basicblocks.
 5. The computing device of claim 1, wherein to determine theprobabilistic hash signature for each normalized code segment using then-gram analysis algorithm comprises to, for each normalized codesegment: clear all bits of a signature bit vector associated with acorresponding normalized code segment; generate a plurality of n-gramsas a function of the corresponding normalized code segment, using asliding window of width n; and for each n-gram of the plurality ofn-grams: (i) apply a hash function to a corresponding n-gram to generatea hash value, (ii) determine a bit index as a function of the hashvalue, and (iii) set a bit within the signature bit vector at the bitindex.
 6. The computing device of claim 1, further comprising a filterhardware module to: filter the plurality of normalized code segments asa function of a plurality of known-good normalized code segments toidentify unfiltered normalized code segments of the normalized codesegments; wherein to determine the similarity value for each pair ofnormalized code segments comprises to determine the similarity value foreach pair of unfiltered normalized code segments.
 7. The computingdevice of claim 6, wherein: to filter the plurality of normalized codesegments comprises to determine whether the probabilistic hash signatureof the normalized code segment is included in a filter bit vectorgenerated as a function of the plurality of known-good normalized codesegments; the static analysis hardware module is further to identify asecond plurality of code segments within a collection of known-goodsoftware; the normalization hardware module is further to, for each codesegment of the second plurality of code segments, extract a first dataelement from each computer instruction of the code segment to generate aknown-good normalized code segment within the plurality of known-goodnormalized code segments; and the filter hardware module is further todetermine the filter bit vector as a function of the plurality ofknown-good normalized code segments.
 8. The computing device of claim 7,wherein to determine the filter bit vector comprises to determine thefilter bit vector as a function of the plurality of known-goodnormalized code segments using the n-gram analysis algorithm.
 9. Thecomputing device of claim 6, further comprising a visualization hardwaremodule to generate a graph including the similarity values, wherein togenerate the graph including the similarity values comprises to: createa node for each normalized code segment; and for each pair of unfilterednormalized code segments, create an edge between the nodes for thecorresponding pair of unfiltered normalized code segments if thesimilarity value of the corresponding pair of unfiltered normalized codesegments has a predefined relationship with a threshold similarityvalue.
 10. The computing device of claim 9, wherein the visualizationhardware module is further to cluster the graph using a force-basedclustering layout algorithm.
 11. The computing device of claim 10,wherein to cluster the graph comprises to interactively display thegraph using a display of the computing device.
 12. The computing deviceof claim 1, wherein to determine the similarity value comprises todetermine an approximate Jaccard index of the probabilistic hashsignatures of each pair of normalized code segments.
 13. A computingdevice for software analysis, the computing device comprising: a staticanalysis hardware module to identify a plurality of code segments withina collection of software; a signature hardware module to determine aprobabilistic hash signature for each code segment using an n-gramanalysis algorithm; a filter hardware module to (i) filter the pluralityof code segments as a function of a plurality of known-good codesegments to identify unfiltered code segments of the code segments,wherein to filter the plurality of code segments comprises to determinewhether the probabilistic hash signature of the code segment is includedin a filter bit vector generated as a function of the plurality ofknown-good code segments, and (ii) determine the filter bit vector as afunction of the known-good code segments using the n-gram analysisalgorithm; and a similarity hardware module to determine a similarityvalue for each pair of unfiltered code segments as a function of theprobabilistic hash signatures of the corresponding pair of unfilteredcode segments, wherein each similarity value is indicative of codesimilarity between the corresponding pair of unfiltered code segments.14. (canceled)
 15. One or more non-transitory, computer-readable storagemedia comprising a plurality of instructions that in response to beingexecuted cause a computing device to: identify a plurality of codesegments within a collection of software; extract, for each code segmentof the plurality of code segments, a first data element from eachcomputer instruction of the corresponding code segment to generate anormalized code segment; determine a probabilistic hash signature foreach normalized code segment using an n-gram analysis algorithm; anddetermine a similarity value for each pair of normalized code segmentsas a function of the probabilistic hash signatures of the correspondingpair of normalized code segments, wherein each similarity value isindicative of code similarity between the corresponding pair ofnormalized code segments.
 16. The one or more non-transitory,computer-readable storage media of claim 15, wherein to extract thefirst data element of each computer instruction comprises to extract afirst byte of each computer instruction.
 17. The one or morenon-transitory, computer-readable storage media of claim 15, whereineach code segment of the plurality of code segments comprises a basicblock.
 18. The one or more non-transitory, computer-readable storagemedia of claim 15, wherein to determine the probabilistic hash signaturefor each normalized code segment using the n-gram analysis algorithmcomprises, for each normalized code segment to: clear all bits of asignature bit vector associated with the corresponding normalized codesegment; generate a plurality of n-grams as a function of thecorresponding normalized code segment, using a sliding window of widthn; and for each n-gram of the plurality of n-grams: (i) apply a hashfunction to the corresponding n-gram to generate a hash value, (ii)determine a bit index as a function of the hash value, and (iii) set abit within the signature bit vector at the bit index.
 19. The one ormore non-transitory, computer-readable storage media of claim 15,further comprising a plurality of instructions that in response to beingexecuted cause the computing device to: filter a plurality of normalizedcode segments as a function of a plurality of known-good normalized codesegments to identify unfiltered normalized code segments of thenormalized code segments; wherein to determine the similarity value foreach pair of normalized code segments comprises to determine thesimilarity value for each pair of unfiltered normalized code segments.20. The one or more non-transitory, computer-readable storage media ofclaim 19, wherein to filter the plurality of normalized code segmentscomprises to determine whether the probabilistic hash signature of thenormalized code segment is included in a filter bit vector generated asa function of the plurality of known-good normalized code segments, andwherein the one or more computer-readable storage media furthercomprises a plurality of instructions that in response to being executedcause the computing device to: identify a second plurality of codesegments within a collection of known-good software; for each codesegment of the second plurality of code segments, extract a first dataelement from each computer instruction of the code segment to generate aknown-good normalized code segment within the plurality of known-goodnormalized code segments; and determine the filter bit vector as afunction of the plurality of known-good normalized code segments. 21.The one or more non-transitory, computer-readable storage media of claim20, wherein to determine the filter bit vector comprises to determinethe filter bit vector as a function of the plurality of known-goodnormalized code segments using the n-gram analysis algorithm.
 22. Theone or more non-transitory, computer-readable storage media of claim 19,further comprising a plurality of instructions that in response to beingexecuted cause the computing device to generate a graph including thesimilarity values, wherein to generate the graph including thesimilarity values comprises to: create a node for each normalized codesegment; and for each pair of unfiltered normalized code segments,create an edge between the nodes for the corresponding pair ofunfiltered normalized code segments if the similarity value of thecorresponding pair of unfiltered normalized code segments has apredefined relationship with a threshold similarity value.
 23. The oneor more non-transitory, computer-readable storage media of claim 15,wherein to determine the similarity value comprises to determine anapproximate Jaccard index of the probabilistic hash signatures of eachpair of normalized code segments.
 24. One or more non-transitory,computer-readable storage media comprising a plurality of instructionsthat in response to being executed cause a computing device to: identifya plurality of code segments within a collection of software; determinea probabilistic hash signature for each code segment using an n-gramanalysis algorithm; filter the plurality of code segments as a functionof a plurality of known-good code segments to identify unfiltered codesegments of the code segments, wherein to filter the plurality of codesegments comprises to determine whether the probabilistic hash signatureof the code segment is included in a filter bit vector generated as afunction of the plurality of known-good code segments; determine thefilter bit vector as a function of the known-good code segments usingthe n-gram analysis algorithm; and determine a similarity value for eachpair of unfiltered code segments as a function of the probabilistic hashsignatures of the corresponding pair of unfiltered code segments,wherein each similarity value is indicative of code similarity betweenthe corresponding pair of unfiltered code segments.
 25. (canceled)